{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2846b614-9343-4404-907a-633e868f61ab",
   "metadata": {},
   "source": [
    "# CNN+LSTM+MultiheadAttention 实现股票多变量时间序列预测（PyTorch版）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bd0265e-a602-46c7-bcd0-59188d204b58",
   "metadata": {},
   "source": [
    "随着金融市场的日益复杂和多变，股票市场的预测成为了金融领域研究的重要课题。传统的股票预测方法，如线性回归、移动平均线等，虽然在一定程度上能够提供有价值的参考，但在处理高维度、非线性的股票数据时，往往显得力不从心。近年来，深度学习技术的快速发展为股票预测提供了新的思路和方法。\n",
    "\n",
    "在深度学习的众多模型中，卷积神经网络（CNN）和长短期记忆网络（LSTM）因其独特的优势而被广泛应用于时间序列预测领域。CNN能够有效捕捉数据的局部特征，尤其对于图像或时间序列数据中的空间结构具有强大的建模能力；而LSTM则通过其内部的记忆单元和门控机制，能够处理序列数据中的长期依赖关系，非常适合于股票这类具有时序特性的数据。然而，尽管CNN和LSTM的结合已经在许多时间序列预测任务中取得了显著的效果，但在面对多变量、高噪声的股票数据时，其预测精度仍有待提高。为此，我们引入了多头注意力机制（Multihead Attention），以进一步提高模型的预测能力。\n",
    "\n",
    "多头注意力机制最初由Google的《Attention is All You Need》一文提出，是Transformer模型的核心组件之一。它通过并行计算多个注意力头，为每个位置的数据提供多个独立的表示，并对其进行加权聚合，从而能够捕捉数据中的多种依赖关系。在股票预测任务中，多头注意力机制可以帮助模型更好地关注不同变量之间的相关性，以及不同时间点之间的依赖关系，从而提高预测的准确性和稳定性。\n",
    "\n",
    "因此，本文提出了一种基于CNN+LSTM+Multihead Attention的股票多变量时间预测模型。该模型首先利用CNN从股票数据中提取局部特征，然后利用LSTM学习这些特征之间的时序关系，最后通过多头注意力机制对关键信息进行加权聚合，得到最终的预测结果。我们期望通过该模型，能够更准确地预测股票市场的走势，为投资者提供有价值的参考。同时，我们也希望本文的研究能够为深度学习在股票预测领域的应用提供新的思路和方向。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c899701-3913-496d-b9f3-89cdd464b9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torchinfo import summary\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a15f5fbe-5ab2-4f94-a1b6-ed2d05b11084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x26fac421fb0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "torch.manual_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fcf0ac-ac8a-44a7-850e-4aa0900ee819",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Data preprocessing</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8e64390-bc88-484f-bd5e-395c8d18623b",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= 'orange'>**pandas.to_datetime**</font> <font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>function converts a scalar, array-like, Series or DataFrame/dict-like to a pandas datetime object.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8575163c-aa09-4fb6-a5ea-2d7c2c421987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.float64'> <class 'str'>\n",
      "<class 'numpy.float64'> <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
      "(755, 6)\n"
     ]
    }
   ],
   "source": [
    "AAPL = pd.read_csv('AAPL.csv')\n",
    "print(type(AAPL['Close'].iloc[0]),type(AAPL['Date'].iloc[0]))\n",
    "# Let's convert the data type of timestamp column to datatime format\n",
    "AAPL['Date'] = pd.to_datetime(AAPL['Date'])\n",
    "print(type(AAPL['Close'].iloc[0]),type(AAPL['Date'].iloc[0]))\n",
    "\n",
    "# Selecting subset\n",
    "cond_1 = AAPL['Date'] >= '2021-04-23 00:00:00'\n",
    "cond_2 = AAPL['Date'] <= '2024-04-23 00:00:00'\n",
    "AAPL = AAPL[cond_1 & cond_2].set_index('Date')\n",
    "print(AAPL.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410f91ce-405b-4453-8ef9-dec59611e4fd",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Data visualisation</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91eb05f-5209-4ced-a1a6-b2253b1f6a12",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>The closing price is the last price at which the stock is traded during the regular trading day. A stock’s closing price is the standard benchmark used by investors to track its performance over time.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42eae829-15c0-43e0-94b4-016cc14f1909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.style.available\n",
    "plt.style.use('_mpl-gallery')\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.title('Close Price History')\n",
    "plt.plot(AAPL['Close'],label='AAPL')\n",
    "plt.ylabel('Close Price USD ($)', fontsize=18)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a75faa-49ad-44cb-8ca2-a010cc563ee9",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Feature Engineering</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74cee356-87c8-47a0-a659-881e8615b89e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置时间回溯窗口大小, time_steps\n",
    "window_size = 60"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9218f559-cadb-4ebd-87c4-dad53c06e766",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#D8BFD8'>The function takes two arguments: the dataset, which is a NumPy array you want to convert into a dataset, and the lookback, which is the number of previous time steps to use as input variables to predict the next time period—in this case, defaulted to 1.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f57c9149-4a61-4c9c-a214-4d95a6dd96a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造序列数据函数，若target为 Close，即索引为 3\n",
    "def create_dataset(dataset, lookback= 1):\n",
    "    X, y = [], []\n",
    "    for i in range(len(dataset)-lookback): \n",
    "        feature = dataset[i:(i+lookback), :]\n",
    "        target = dataset[i + lookback, 3]\n",
    "        X.append(feature)\n",
    "        y.append(target)\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77806a4-e443-4c97-a1ab-f6f38ef74dd1",
   "metadata": {},
   "source": [
    " ## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>Feature Scaling (normalisation)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c7616b-87f9-4279-90f0-b31ee2165201",
   "metadata": {},
   "source": [
    "`MinMaxScaler()` 函数主要用于将特征数据按比例缩放到指定的范围。默认情况下，它将数据缩放到[0, 1]区间内，但也可以通过参数设置将数据缩放到其他范围。在机器学习中，`MinMaxScaler()`函数常用于不同尺度特征数据的标准化，以提高模型的泛化能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bc72431-3223-4c37-bb67-e702e8b9c147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 选取 AAPL[['Open', 'High', 'Low', 'Close']]作为特征, 归一化数据\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(AAPL[['Open', 'High', 'Low', 'Close']].values)\n",
    "\n",
    "# 获取反归一化参数(即原始数据的最小值和最大值)\n",
    "original_min = scaler.data_min_\n",
    "original_max = scaler.data_max_\n",
    "\n",
    "# scale_params是一个包含所有特征反归一化参数的列表或数组，\n",
    "# 其中第四个元素是Close价格的反归一化参数\n",
    "scale_params = original_max - original_min"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c1d7f33-0396-4d7e-a9f6-565b80da9d3e",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>Data set segmentation (TimeSeriesSplit)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9395c0f7-6952-48eb-93dd-74be21c9e81a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(695, 60, 4) (695,)\n"
     ]
    }
   ],
   "source": [
    "# 创建数据集,数据形状为 [samples, time steps, features]\n",
    "X, y = create_dataset(scaled_data, lookback = window_size)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1353fd0-0c8c-4428-8440-3e4c20cb77e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(605, 60, 4) (90, 60, 4) (605,) (90,)\n"
     ]
    }
   ],
   "source": [
    "# 使用TimeSeriesSplit划分数据集，根据需要调整n_splits\n",
    "tscv = TimeSeriesSplit(n_splits=3, test_size=90)\n",
    "# 遍历所有划分进行交叉验证\n",
    "for i, (train_index, test_index) in enumerate(tscv.split(X)):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    # print(f\"Fold {i}:\")\n",
    "    # print(f\"  Train: index={train_index}\")\n",
    "    # print(f\"  Test:  index={test_index}\")\n",
    "\n",
    "# 查看最后一个 fold 数据帧的维度\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dca0c69-189f-438f-86e7-034b23379e7a",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b8a1cf'>Data set tensor (math.)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ea9c7fa-be36-4403-a28e-4b5f5064b1a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([605, 60, 4]) torch.Size([90, 60, 4]) torch.Size([605, 1]) torch.Size([90, 1])\n"
     ]
    }
   ],
   "source": [
    "# 将 NumPy数组转换为 tensor张量\n",
    "X_train_tensor = torch.from_numpy(X_train).type(torch.Tensor)\n",
    "X_test_tensor = torch.from_numpy(X_test).type(torch.Tensor)\n",
    "y_train_tensor = torch.from_numpy(y_train).type(torch.Tensor).view(-1, 1)\n",
    "y_test_tensor = torch.from_numpy(y_test).type(torch.Tensor).view(-1, 1)\n",
    "\n",
    "print(X_train_tensor.shape, X_test_tensor.shape, y_train_tensor.shape, y_test_tensor.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ffdb65-8341-409f-9527-92592095b75e",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= '#cca3cc'>The view() function in PyTorch is used to reshape a tensor object. It’s equivalent to the reshape() function in NumPy and allows us to restructure our data to match the input shape that our BiLSTM model requires. Reshaping the data this way ensures that the BiLSTM model receives the data in the expected format. This is crucial for its ability to model the temporal dependencies in our time series data effectively.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cdbd189-8102-41cc-ae70-424a5e011f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_dataset = TensorDataset(X_test_tensor, y_test_tensor)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f75a73-1a55-41c6-9627-b0ed71c1d4b8",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b8a1cf'>Constructing a time series model</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cdd4e28-eea3-4e26-b2e1-564a93f3aea5",
   "metadata": {},
   "source": [
    "该组合模型能够综合利用 CNN 对局部特征的提取能力、LSTM 对序列数据的长期依赖处理能力以及多头注意力机制对不同特征重要性的动态关注能力，适用于处理具有复杂时空特征的序列数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec949ca-a9a4-4f92-acf9-2c7b42cdef42",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#C2C4E2'>Model construction</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b2a6573-b37f-4f67-8fe0-a3816b7539a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN_LSTM_MultiheadAttention(nn.Module):\n",
    "    def __init__(\n",
    "        self, \n",
    "        in_channels = 4, # 输入特征数量\n",
    "        out_channels = 64, # CNN卷积产生的通道数量用作 LSTM的输入\n",
    "        hidden_size = 128, \n",
    "        num_layers = 1, \n",
    "        kernel_size = 3, # 卷积核数\n",
    "        num_heads = 8, # MultiheadAttention的头数\n",
    "        output_size = 1,\n",
    "    ):\n",
    "        super(CNN_LSTM_MultiheadAttention, self).__init__()\n",
    "        \n",
    "        self.num_layers = num_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1)\n",
    "\n",
    "        self.lstm = nn.LSTM(out_channels, hidden_size, num_layers, batch_first=True)\n",
    "        self.lstm_fc = nn.Linear(hidden_size, hidden_size) # LSTM 输出的线性变换层 self.lstm_fc\n",
    "        \n",
    "        self.multihead_attn = nn.MultiheadAttention(hidden_size, num_heads, batch_first=True)\n",
    "        self.fc_out = nn.Linear(hidden_size* 2, output_size)\n",
    "\n",
    "    def forward(self, x): # x的形状为(batch_size, num_timesteps, input_size)\n",
    "        # 初始化隐藏状态(hidden state)和单元状态(cell state)为全零张量\n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)  \n",
    "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device) \n",
    "        \n",
    "        # CNN层，假设 x的形状为 (batch_size, num_timesteps, in_channels)\n",
    "        x = x.permute(0, 2, 1) # (batch_size, in_channels, num_timesteps) -> CNN\n",
    "        x = self.conv(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.maxpool(x)\n",
    "        x = x.permute(0, 2, 1) # (batch_size, num_timesteps, out_channels)\n",
    "        # LSTM层\n",
    "        out, _ = self.lstm(x)\n",
    "        \n",
    "        # Multihead Attention层，LSTM最后一个时间步的输出作为 query\n",
    "        query, key, value = out[:, -1, :], self.lstm_fc(out), self.lstm_fc(out)\n",
    "        # MultiheadAttention需要query, key, value的形状为(batch_size, sequence_length, embed_dim)\n",
    "        attn_output, attn_weights = self.multihead_attn(query.unsqueeze(1), key, value)\n",
    "        \n",
    "        # 将Multihead Attention的输出与原始LSTM输出结合，这里我们简单地将它们相加并通过一个线性层\n",
    "        combination = torch.cat((query.squeeze(1), attn_output.squeeze(1)), dim=1)\n",
    "        output = self.fc_out(combination)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4dc50d-1699-4212-8f9b-2c729f960aed",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3c8f25b4-9a7a-41bd-a531-dce3a2d8a2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = SP(num_features, hidden_dim, num_layers, num_heads)\n",
    "model = CNN_LSTM_MultiheadAttention()\n",
    "criterion = torch.nn.MSELoss() # 定义均方误差损失函数\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # 定义优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d8abc69-890b-4113-b900-467cf12ddae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "==========================================================================================\n",
       "Layer (type:depth-idx)                   Output Shape              Param #\n",
       "==========================================================================================\n",
       "CNN_LSTM_MultiheadAttention              [32, 1]                   --\n",
       "├─Conv1d: 1-1                            [32, 64, 60]              832\n",
       "├─ReLU: 1-2                              [32, 64, 60]              --\n",
       "├─MaxPool1d: 1-3                         [32, 64, 59]              --\n",
       "├─LSTM: 1-4                              [32, 59, 128]             99,328\n",
       "├─Linear: 1-5                            [32, 59, 128]             16,512\n",
       "├─Linear: 1-6                            [32, 59, 128]             (recursive)\n",
       "├─MultiheadAttention: 1-7                [32, 1, 128]              66,048\n",
       "├─Linear: 1-8                            [32, 1]                   257\n",
       "==========================================================================================\n",
       "Total params: 182,977\n",
       "Trainable params: 182,977\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (Units.MEGABYTES): 190.19\n",
       "==========================================================================================\n",
       "Input size (MB): 0.03\n",
       "Forward/backward pass size (MB): 6.78\n",
       "Params size (MB): 0.47\n",
       "Estimated Total Size (MB): 7.28\n",
       "=========================================================================================="
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(model, (32, window_size, 4)) # batch_size, seq_len(time_steps), input_size(in_channels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "904037ae-f7c1-416a-b3a6-b855dd7fb35c",
   "metadata": {},
   "source": [
    "# 4. 模型训练与可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45825b55-5af7-4da8-b500-ec6844cd1a45",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/20: 100%|███████████████████████████████| 19/19 [00:01<00:00, 11.14it/s]\n",
      "Epoch 2/20: 100%|███████████████████████████████| 19/19 [00:02<00:00,  9.26it/s]\n",
      "Epoch 3/20: 100%|███████████████████████████████| 19/19 [00:01<00:00,  9.97it/s]\n",
      "Epoch 4/20: 100%|███████████████████████████████| 19/19 [00:02<00:00,  9.34it/s]\n",
      "Epoch 5/20: 100%|███████████████████████████████| 19/19 [00:02<00:00,  8.58it/s]\n",
      "Epoch 6/20: 100%|███████████████████████████████| 19/19 [00:02<00:00,  9.15it/s]\n",
      "Epoch 7/20: 100%|███████████████████████████████| 19/19 [00:02<00:00,  9.45it/s]\n",
      "Epoch 8/20: 100%|███████████████████████████████| 19/19 [00:01<00:00,  9.83it/s]\n",
      "Epoch 9/20: 100%|███████████████████████████████| 19/19 [00:02<00:00,  9.19it/s]\n",
      "Epoch 10/20: 100%|██████████████████████████████| 19/19 [00:01<00:00,  9.82it/s]\n",
      "Epoch 11/20: 100%|██████████████████████████████| 19/19 [00:01<00:00,  9.89it/s]\n",
      "Epoch 12/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 10.33it/s]\n",
      "Epoch 13/20: 100%|██████████████████████████████| 19/19 [00:01<00:00,  9.89it/s]\n",
      "Epoch 14/20: 100%|██████████████████████████████| 19/19 [00:02<00:00,  9.19it/s]\n",
      "Epoch 15/20: 100%|██████████████████████████████| 19/19 [00:01<00:00,  9.96it/s]\n",
      "Epoch 16/20: 100%|██████████████████████████████| 19/19 [00:01<00:00, 10.05it/s]\n",
      "Epoch 17/20: 22it [00:01, 14.09it/s]                                            "
     ]
    }
   ],
   "source": [
    "train_losses = [] # 初始化列表来存储损失值\n",
    "test_losses = [] # 初始化列表来存储损失值\n",
    "best_loss = float('100') # 初始化为一个很大的值\n",
    "best_model = None # 初始化最佳模型为None\n",
    "\n",
    "# 训练循环\n",
    "num_epochs = 20  # 假设我们要训练 20个 epoch\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # 初始化训练进程\n",
    "    train_loss = 0.0  # 初始化每个epoch的损失总和\n",
    "\n",
    "    pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\",ncols=80)\n",
    "    for batch_idx, (data, target) in enumerate(pbar):\n",
    "        optimizer.zero_grad()  # 将优化器中所有参数的梯度归零\n",
    "        output = model(data)  # 每个批次的预测值\n",
    "        loss = criterion(output, target)  # 计算模型损失值(y_train_pred 与 y_train)\n",
    "        # 累加每个批次的损失\n",
    "        train_loss += loss.item() * data.size(0)\n",
    "        # 反向传播和优化\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # 更新进度条(显示当前批次的损失)\n",
    "        pbar.update()\n",
    "\n",
    "    # 计算当前epoch的平均损失\n",
    "    average_loss = train_loss / len(train_loader.dataset)\n",
    "    train_losses.append(average_loss)\n",
    "\n",
    "    model.eval()  # 将模型设置为评估模式\n",
    "    with torch.no_grad():  # 不计算梯度以节省内存和计算资源\n",
    "        test_loss = 0.0\n",
    "        for data, target in test_loader:\n",
    "            output = model(data) # 每个批次的预测值\n",
    "            loss = criterion(output, target)\n",
    "            test_loss += loss.item() * data.size(0)\n",
    "\n",
    "        # 计算测试集的平均损失\n",
    "        test_loss = test_loss / len(test_loader.dataset)\n",
    "        test_losses.append(test_loss)\n",
    "\n",
    "    # 如果测试损失比之前的最小值小，则更新最佳模型权重\n",
    "    if test_loss < best_loss:\n",
    "        best_loss = test_loss\n",
    "        best_model = model.state_dict() \n",
    "\n",
    "# 训练循环结束后保存最佳模型\n",
    "if best_model is not None:\n",
    "    torch.save(best_model, 'model_best.pth')\n",
    "    print(f'Best model saved with test loss {best_loss:.4f}')\n",
    "else:\n",
    "    print('No model saved as no improvement was made.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23efb6fe-6f74-4edc-9280-485dd949d393",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制损失图\n",
    "plt.figure(figsize=(18, 6))\n",
    "plt.plot(train_losses, label='Train Loss')\n",
    "plt.plot(test_losses, label='Test Loss')\n",
    "plt.title('Training and Test Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac88ed6f-f776-4127-a78a-6328ce80b288",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#b89bd5'>Model evaluation</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d3d1c3-6311-46f8-8d9c-96109dc34670",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b89bd5'>Evaluation indicators (MAE、RMSE、MAPE、R<sup>2</sup>)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ad9504-a566-4363-bf4d-33834bcebdd7",
   "metadata": {},
   "source": [
    "<font face= 'Comic Sans MS' size= 3 color= 'red'>These metrics each evaluate different aspects of the model’s performance:\n",
    " - <font face= 'Comic Sans MS' size= 3 color= 'orange'>RMSE: This metric calculates the square root of the average squared differences between the predicted and actual values. It gives a higher penalty for large errors.\n",
    " - <font face= 'Comic Sans MS' size= 3 color= 'gold'>MAPE: This metric computes the mean of the absolute percentage difference between the actual and predicted values. It expresses the average absolute error in terms of percentage, which can be useful when you want to understand the relative prediction error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa3cdc83-8a71-426e-b32b-176262104158",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载最佳模型\n",
    "model.load_state_dict(torch.load('model_best.pth'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de24041f-2892-46f2-9268-be5bf199066c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_percentage_error(y_true, y_pred):\n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    mask = y_true != 0\n",
    "    return np.mean(np.abs((y_true[mask] - y_pred[mask]) / y_true[mask])) * 100 if mask.any() else 0\n",
    "\n",
    "# 将模型设置为评估模式\n",
    "model.eval()\n",
    "y_pred_all = []\n",
    "y_true_all = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    pbar = tqdm(test_loader, desc='Evaluating')\n",
    "    for data, target in pbar:\n",
    "        y_pred = model(data).detach().cpu().numpy()  # 确保张量在CPU上\n",
    "        y_true = target.detach().cpu().numpy()  # 确保张量在CPU上\n",
    "        y_pred_all.append(y_pred)\n",
    "        y_true_all.append(y_true)\n",
    "        pbar.update()\n",
    "\n",
    "# 合并所有批次的预测和真实值\n",
    "y_pred_all = np.concatenate(y_pred_all)\n",
    "y_true_all = np.concatenate(y_true_all)\n",
    "\n",
    "mae = np.mean(np.abs(y_pred_all - y_true_all))\n",
    "print(f\"MAE: {mae:.4f}\")\n",
    "\n",
    "rmse = np.sqrt(np.mean((y_pred_all - y_true_all) ** 2))\n",
    "print(f\"RMSE: {rmse:.4f}\")\n",
    "\n",
    "mape = mean_absolute_percentage_error(y_true_all, y_pred_all)\n",
    "print(f\"MAPE: {mape:.4f}%\")\n",
    "\n",
    "y_bar = np.mean(y_true_all)  # 真实值的平均值\n",
    "SS_res = np.sum((y_true_all - y_pred_all) ** 2)  # 残差平方和\n",
    "SS_tot = np.sum((y_true_all - y_bar) ** 2)  # 总平方和\n",
    "R2 = 1 - (SS_res / SS_tot)\n",
    "print(f\"R²: {R2:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f548c4-7389-431e-93d4-733f9c8a8e48",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b89bd5'>Inverse normalisation (.math)</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a11de9-bdfd-4f0c-b88b-63e86339b4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取Close价格的反归一化参数\n",
    "close_denorm_param = scale_params[3]  # 假设Close是第四个特征\n",
    "\n",
    "# 如果反归一化包括一个偏移量shift，需要再加上 shift_param\n",
    "# denormalized_predictions = (predictions[:, 0] * scale_param) + shift_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60d2eb4-2fc8-4513-8896-d403f51d5397",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将训练预测 Close价格的反归一化\n",
    "y_train_pred = model(X_train_tensor).detach().numpy()\n",
    "y_train_denormalized_predictions = (y_train_pred * scale_params[3]) + original_min[3]\n",
    "\n",
    "# 将测试预测 Close价格的反归一化\n",
    "y_test_pred = model(X_test_tensor).detach().numpy()\n",
    "y_test_denormalized_predictions = (y_test_pred * scale_params[3]) + original_min[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "185f6c00-db6d-41fe-846e-c27b3633c680",
   "metadata": {},
   "source": [
    "## <font face= 'Comic Sans MS' size= 5 color= '#b89bd5'>Visualisation of results</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6626cb28-1339-459e-a892-77244cda941b",
   "metadata": {},
   "source": [
    "计算训练预测与测试预测的绘图数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eed2140-375b-49a8-91c5-b2c967ffe194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredict = AAPL[window_size:X_train.shape[0]+X_train.shape[1]]\n",
    "trainPredictPlot = trainPredict.assign(TrainPrediction= y_train_denormalized_predictions)\n",
    "\n",
    "testPredict = AAPL[X_train.shape[0]+X_train.shape[1]:]\n",
    "testPredictPlot = testPredict.assign(TestPrediction= y_test_denormalized_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f5636b-ec03-473c-827a-4ec81f1c07a2",
   "metadata": {},
   "source": [
    "绘制模型收盘价格的原始数据与预测数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "325fe284-2dc6-4483-a080-b2e0cb4165b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize the data\n",
    "plt.figure(figsize=(18,6))\n",
    "plt.title('CNN-LSTM-MultiheadAttention Close Price Validation')\n",
    "plt.plot(AAPL['Close'], color='blue', label='original')\n",
    "plt.plot(trainPredictPlot['TrainPrediction'], color='orange',label='Train Prediction')\n",
    "plt.plot(testPredictPlot['TestPrediction'], color='red', label='Test Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862b7057-9466-47d4-af54-2bd4d37bbc2d",
   "metadata": {},
   "source": [
    "# <font face= 'Comic Sans MS' size= 6 color= '#C2C4E2'>Model prediction</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d48932bf-9519-49e5-85cd-510a4925000e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 假设latest_closes是一个包含最新window_size个收盘价的列表或数组\n",
    "latest_closes = AAPL[['Open', 'High', 'Low', 'Close']][-window_size:].values\n",
    "scaled_latest_closes = scaler.transform(latest_closes)\n",
    "tensor_latest_closes = torch.from_numpy(scaled_latest_closes).type(torch.Tensor).view(1, window_size, 4) \n",
    "print(tensor_latest_closes.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b8b310f-524a-416b-a111-6e498d5a0a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用模型预测下一个时间点的收盘价\n",
    "next_close_pred = model(tensor_latest_closes).detach().numpy()\n",
    "next_close_denormalized_pred = (next_close_pred * scale_params[3]) + original_min[3]\n",
    "next_close_denormalized_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "073bbaa6-304c-4ce5-a136-dbb58d23de05",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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